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INFORMATION PROCESSING IN AGRICULTURE 3 (2016) 190–205

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Optimization of energy consumption andenvironmental impacts of chickpea productionusing data envelopment analysis (DEA) and multiobjective genetic algorithm (MOGA) approaches

http://dx.doi.org/10.1016/j.inpa.2016.07.0022214-3173 � 2016 China Agricultural University. Publishing services by Elsevier B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abbreviations: GHG, green house gas; LCA, life cycle assessment; DEA, data envelopment analysis; TE, technical efficiencytechnical efficiency; SEF, scale efficiency; DMU, decision making unit; CRS, constant returns to scale; VRS, variable returns togenetic algorithm; EA, evolutionary algorithm; MOGA, multi objective genetic algorithm; VIF, variance inflation factor; FYMmanure; ME, machine energy; IE, irrigation energy; ER, energy ratio; EP, energy productivity; SE, specific energy; NEG, net energdirect energy; IDE, indirect energy; RE, renewable energy; RI, respiratory inorganics; NRE, non renewable energy; FU, functionalife cycle inventory; LCIA, life cycle impact assessment; ACP, acidification potential; EUP, eutrophication potential; GWP, globapotential; HTP, human toxicity potential; TEP, terrestrial ecotoxicity potential* Corresponding authors. Fax: +98 2632808138.E-mail addresses: aakram@ut.ac.ir (A. Akram), khanali@ut.ac.ir (M. Khanali).

Peer review under responsibility of China Agricultural University.

Behzad Elhami, Asadollah Akram *, Majid Khanali *

Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

A R T I C L E I N F O A B S T R A C T

Article history:

Received 25 February 2016

Received in revised form

30 May 2016

Accepted 15 July 2016

Available online 9 August 2016

Keywords:

Data envelopment analysis

Energy

Life cycle assessment

Multi objective genetic algorithm

Energy consumption in agricultural products and its environmental damages has increased

in recent centuries. Life cycle assessment (LCA) has been introduced as a suitable tool for

evaluation environmental impacts related to a product over its life cycle.

In this study, optimization of energy consumption and environmental impacts of chickpea

production was conducted using data envelopment analysis (DEA) and multi objective

genetic algorithm (MOGA) techniques. Data were collected from 110 chickpea production

enterprises using a face to face questionnaire in the cropping season of 2014–2015. The

results of optimization revealed that, when applying MOGA, optimum energy requirement

for chickpea production was significantly lower compared to application of DEA technique;

so that, total energy requirement in optimum situation was found to be 31511.72 and

27570.61 MJ ha�1 by using DEA and MOGA techniques, respectively; showing a reduction

by 5.11% and 17% relative to current situation of energy consumption. Optimization of envi-

ronmental impacts by application of MOGA resulted in reduction of acidification potential

(ACP), eutrophication potential (EUP), global warming potential (GWP), human toxicity

potential (HTP) and terrestrial ecotoxicity potential (TEP) by 29%, 23%, 10%, 6% and 36%,

respectively. MOGAwas capable of reducing the energy consumption frommachinery, farm-

yard manure (FYM) diesel fuel and nitrogen fertilizer (the mostly contributed inputs to the

environmental emissions) by 59%, 28.5%, 24.58% and 11.24%, respectively. Overall, the

MOGA technique showed a superior performance relative to DEA approach for optimizing

energy inputs and reducing environmental impacts of chickpea production system.

� 2016 China Agricultural University. Publishing services by Elsevier B.V. This is an open

access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-

nd/4.0/).

; PTE, purescale; GA,, farmyardy gain; DE,l unit; LCI,l warming

I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 191

1. Introduction

Chickpea (Cicer arietinum L.), commonly known as garbanzo, is

a type of pulse with one seedpod containing two or three peas

[1]. This herbage plant is usually grown in the summer and

has atmospheric nitrogen-fixing root nodules [2].

There are two main varieties of chickpea including Kabuli

and Desi; Kabuli variety has light color, large seeds and a

smooth coat; while, Desi variety, with a rough coat, has smal-

ler and darker seeds cultivated mostly in Ethiopia, Mexico and

humid regions of Iran [1].

Based on the FAO statistics [3], Iran was the 7th largest pro-

ducer of chickpea after India, Australia, Pakistan, Turkey,

Myanmar and Ethiopia, respectively.

The average chickpea yield in Iran is 500 kg ha�1; while the

world average yield is 900 kg ha�1 [4]. Total production of

chickpea crop in Iran was about 176,000 tons in 2011. The

majority of Iranian chickpea, more than 30% is produced in

Esfahan province [5].

In developing countries like Iran, agricultural mechaniza-

tion is essential to support the economic growth and meet

the food demand for growing population. Energy use effi-

ciency in agriculture is one of the conditions for sustainable

agricultural production, since it preserves fossil resources

and decreases air pollution [6]. However, emissions from agri-

culture, shows a growing trend during the recent years due to

a high application of synthetic nitrogen, direct energy inputs

and intensive use of farm machinery in Iranian agriculture

[7].

Agricultural greenhouse gas (GHG) emissions account for

10–12% of all manmade GHG emissions [8]. Assessing the

impact at broader space and temporal scales is what life cycle

assessment (LCA) does by considering the production and

transport of inputs and the resulted emissions in a certain

product or service system [9]. LCA has been applied more

widely in agricultural and industrial fields [10–13]. For exam-

ple, Iriarte et al. [14] explored LCA of sunflower and rapeseed

production systems in Chile. They showed that rapeseed pro-

duction had a higher environmental impact than sunflower

growing in most impact categories. In another study in Italy,

environmental performance of tomato, melon, pepper, cherry

tomato, and zucchini production in different greenhouse

typologies (tunnel and pavilion) were assessed. Their investi-

gations showed that, the use of pavilions can allow the sea-

sonal rotation of different types of cultivations, with

sensible reduction of impacts [15].

Data envelopment analysis (DEA) is a mathematical model

that is used extensively in many settings for measuring the

efficiency and benchmarking of decision making units

(DMUs) [16]. DEA is a data-driven frontier analysis technique

that floats a piecewise linear surface to rest on top of the

empirical observations, considered as efficient frontier [17].

In contrast to the parametric methods, DEA does not require

a function to relate inputs and outputs [18]. Reig-Martınez

and Picazo-Tadeo [19] reported that DEA was a useful tool to

improve the productive efficiency of farms. Mohammadi

et al. [20] investigated optimization of energy inputs for

kiwifruit production in Golestan province of Iran. They found

that total energy input could be saved by 12.17%. Also, opti-

mization of energy use improved the energy use efficiency,

specific energy (SE) and net energy gain (NEG) by 13.86%,

12.17% and 22.56%, respectively. Chauhan et al. [21] used

DEA to assess the efficiencies of farmerswith regard to energy

use in rice farming in India. In this study, the technical effi-

ciency (TE), pure technical efficiency (PTE) and scale efficiency

(SEF) of farmers were estimated at 0.83, 0.92 and 0.77,

respectively.

Genetic algorithm (GA) is a flexible scheduling and opti-

mization technique based on the natural selection process

[22]. It finds the problem solutions by using methods inspired

by natural evolutions, such as inheritance, mutation, selec-

tion and crossover [23]. Much different from single-objective

problem, it is complicated to minimize or maximize all objec-

tive functions concurrently when objective functions are in

trade-off relationship. Nevertheless, many tribulations in

agricultural and engineering domains are multi-objective

optimization type [24]. A literature review demonstrates that,

some researchers have reported the valuable application of

multi-objective genetic algorithm (MOGA) in resource man-

agement of cropping systems [23,25,26]. In Italy, Cordeschi

et al. [27] developed the optimal minimum-energy scheduler

for the joint adaptive load balancing and provisioning of the

computing-plus-communication resources. The average

energy savings of the proposed scheduler over the static

and hybrid ones may be larger than 60% and 25%, respec-

tively, even when the peak-to-mean ratio (PMR) of the offered

workload is less than two. Also, the corresponding average

energy loss with respect to the corresponding sequential

scheduler equipped with perfect knowledge of the future

workload is typically limited up to 4–6%.

Khoshnevisan et al. [28] employed the MOGA technique to

minimize global warming potential (GWP), respiratory inor-

ganics (RI) and non-renewable energy use (NRE) of water-

melon production. The results showed that a reduction of

27% in RI and 35% in GWand NRE can occur if an appropriate

combination of resources is used in watermelon production.

The difference between DEA and evolutionary algorithms

(EA), such as MOGA, is that DEA approach not able to calcu-

late global optimum values. In DEA approach the optimum

values are obtained on the basis of units under consideration

though in this method are not determined global optimum. In

the other words, the sole purpose of the study in DEA is to

select the DMU which consumed energy efficiently in com-

parison with all DMUs under consideration [23]. To consider

different objectives in optimization techniques and find glo-

bal optimum solutions, MOGA can be employed.

The sustainable production of chickpea in Iran requires

the consideration of energy flow and environmental impacts

in the production systems. However, to the best of knowledge

of the authors, no previous analytical work has been reported

on the energy consumption and environmental impacts of

chickpea production in Iran.

Therefore, the general objectives of this study are as

follows:

Table 1 – Energy equivalent of inputs and output in chickpeaproduction.

Input–output (unit) Energy equivalent(MJ per unit)

References

1. InputsSeed (kg) 14.7 [32,46]Chemical fertilizer (kg)

Nitrogen (N) 78.1 [32,46]Phosphate (P2O5) 17.4 [30,32]Potassium (K2O) 13.7 [30,32]FYM (kg) 0.3 [29]

Machinery (kg)Tractor 138 [30,32]Plow 180 [32]Disk 149 [32]Boundaries 160 [32]Leveler 149 [32]Planter 133 [32]Sprayer 129 [32]Rotary Hoes 148 [32]Thrashing (h) 62.7 [32]

Chemicals (kg)Herbicide 238 [44]Insecticide 101.2 [44]Fungicide 216 [58]Diesel (L) 47.8 [44]Labor (h) 1.96 [6,44]Electricity (kWh) 11.93 [21,54]

2. Output (kg)Chickpea 14.7 [32,46]

192 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

1. Calculation the energy indices (energy ratio, productivity

energy, specific energy and net energy) for chickpea crop

in the study region.

2. Estimation of some environmental impact (GWP, ACP, HTP,

TEP and EUP) using life cycle assessment.

3. Determine efficient and inefficient units of energy con-

sumption and their emissions by using DEA.

4. The using of MOGA to optimize energy consumption and

environmental impacts.

5. Comparison DEA and MOGA models to optimize energy

consumption and environmental impacts.

6. Presenting some Strategies in order to achieve sustainable

agricultural with the lowest input consumption and lowest

emissions level.

2. Materials and methods

2.1. Study region and data collection

The study was carried out in Esfahan province of Iran during

2014–2015 cropping season. Esfahan province has 6.5% of

total area of the Iran and is located in the center of the coun-

try, within 30� 420 and 34� 300 north latitude and 49� 360 and 55�320 east longitude. The required data was collected from 110

chickpea producers by using face to face survey method.

The number of chickpea producers was calculated using

Cochran method as follows [29]:

n ¼ N� S2 � t2

ðN� 1Þd2 þ ðS2 � t2Þ ð1Þ

In this formula, ‘n’ is the required sample size, ‘N’ is the num-

ber of all chickpea farms in target population, ‘S’ is the stan-

dard deviation in the pre-tested data, ‘t’ is the t value at 95%

confidence limit (1.96) and ‘d’ is the permissible error which

was defined to be 5% for a confidence level of 95%.

2.2. Energy analysis

In this study, the inputs used for the chickpea production

were seed amounts, human labor, electricity, irrigation, diesel

fuel, machinery, chemicals, chemical fertilizers and farmyard

manure (FYM); while output was chickpea yield, calculated

per hectare. The energy associated with all inputs except for

irrigation and machinery was estimated directly by multiply-

ing the corresponding activity data by the appropriate energy

equivalent (Table 1).

The energy of irrigation was calculated as follows [30]:

IE ¼ d� g�H� Qg1 � g2

ð2Þ

where ‘IE’ is irrigation energy (J ha�1), ‘d’ is the water density

(1000 kg m�3), ‘g’ is the acceleration of gravity (9.81 m s�2), ‘H’

is the total dynamic head (m), ‘Q’ is the overall amount of

water, including losses by evaporation, drainage run-off, etc.

(m3 ha�1), ‘ g1 ’ is the pump efficiency and ‘ g2 ’ is the overall

efficiency of the power device, electric or diesel.

The machinery energy was calculated as follows [30]:

ME ¼ G�Mp � t

Tð3Þ

where, ‘ME’ is the machinery energy (MJ ha�1), ‘G’ is mass of

machine (kg), ‘Mp’ is energy equivalent of machinery (Table 1),

‘t’ is the time that machine used per unit area (h ha�1) and ‘T’

is the economic life time of machine (h).

Therefore, the energy ratio (ER, energy use efficiency),

energy productivity (EP), SE and NEGwere calculated as follow

[29,30].

ER ¼ Output energy ðMJ ha�1ÞInput energy ðMJ ha�1Þ ð4Þ

EP ¼ Chickpea output ðkg ha�1ÞEnergy input ðMJ ha�1Þ ð5Þ

SE ¼ Energy input ðMJ ha�1ÞChickpea output ðkg ha�1Þ ð6Þ

NEG ¼ Output energy ðMJ ha�1Þ� Input energy ðMJ ha�1Þ ð7Þ

ER is computed by the ratio of input fossil fuel energy and

output food energy; or in other words, shows the efficient use

of energy in crop production. EP prepared quantitative data on

how much yield is obtained per unit of the consumed energy.

SE is a measure of the amount of input energy per unit of

obtained product. NEG is specified as the difference between

the energy expended to harvest an energy source and the

amount of energy gained from that harvest.

For the growth and development of energy consumption in

agriculture, it can be divided into direct energy (DE), indirect

I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 193

energy (IDE), renewable energy (RE) and NRE [31]. The DE cov-

ers human labor, diesel, electricity and water used in chick-

pea production; while IDE included energy embodied in

seeds, chemical fertilizers, chemicals, FYM and machinery.

The RE consists of human labor, seeds, FYM andwater for irri-

gation and NRE includes diesel, electricity, chemicals, chemi-

cal fertilizers and machinery [32].

2.3. LCA analysis

LCA can be defined as a systematic inventory and analysis of

the environmental impacts that are caused by a product or

process starting from the extraction of rawmaterials, produc-

tion, use, etc. (from cradle to grave). A LCA study is divided

into four phases including, definition of the goal and scope,

compiling an inventory of relevant inputs and outputs, evalu-

ating the potential environmental impacts associated with

inputs and outputs and interpreting the results of the inven-

tory and impact phases in relation to the objectives of the

study (Fig. 1) [33,34].

In an LCA, the goal and scope of the study is stating stage.

The goal of the LCA in this study is the computation of some

environmental impacts of chickpea production by considera-

tion of agricultural proceedings, materials and energy inputs

used during the crop production.

In LCA, the system boundary is a part of the scoping defi-

nition stage and it is a key factor that can affect the results of

LCA studies [35]. The reactions between unit processes are

not necessarily straight, but may also be affected by changes

in the market mechanism, i.e. supply and demand, that con-

nects the processes [15]. In this study, the farm gate was cho-

sen as the system boundary and the environmental impacts

were evaluated from inputs used that are entered to farm

up to harvested chickpea crop (Fig. 2).

With application of LCA to agricultural processes, different

functional units (FUs) can be selected. In many LCA studies of

agricultural production systems, the FU is land-based (e.g.,

1 ha) [26]. Nevertheless, the mass-based functional unit; e.g.

Traditional

Goal an

Inventory

Impact A

New Framework

DEA

Or

GA

Fig. 1 – LCA framework with regard to DEA and GA Techniques.

and GA) Adapted from Khoshnevisan et al. [28].

1 ton of crop produce; is prevalent in LCA studies of agricul-

tural systems [36,37]. Thus, two functional units were chosen:

mass-based (1 ton of produced chickpea) and land-based (per

ha).

Life cycle inventory (LCI) considers the energy, resources

consumption and emissions. The detailed quantitative data

for chickpea production system upon which the analysis

was based are summarized in Table 2.

Inventory data for the production of chemical fertilizers

came from the EcoInvent�2.0 database [38]. Erickson et al.

[39] indicated that 30% of N fertilizer leaches deeper down

into the soil. In this study, insecticides, fungicides and herbi-

cides were classified as a single category referred to as ‘‘pesti-

cides”. The inventory data for pesticides was taken from the

EcoInvent�2.0 database [38].

Based on the literature, it has been suggested that 30–50%

of the total sprayed pesticides be accounted as emissions into

the air [9]. Electrical pumps were the only use of electricity in

chickpea production system in the studied area. It was

assumed that, the source of electricity in power plants was

natural gas. In chickpea production system, all farms applied

diesel for agricultural machinery, especially by tractors and

plows during farm operations. The emitted pollution for back-

ground data (exploration, refining and combustion of fossil

fuels) was adapted from the literature [38].

The aim of life cycle impact assessment (LCIA) is interpre-

tation of the LCI data. There are various methods globally for

categorizing and characterizing the life cycle environmental

impacts including factors of characterization, normalization,

weighting and damaging [40].

In this study, we used characterization and weighting fac-

tors. A literature review revealed that CML2 baseline 2000 V2/-

world developed by the Institute of Environmental Science of

Leiden University is commonly used in LCA studies of agricul-

tural production [41]. The present study considers five LCA

impacts in the CML2 baseline 2000 V2.05/Netherlands, 1997/

characterization method that are shown in Table 3. Impact

categories have been considered in this study, play the most

Interpretation

Framework

d Scope

Analysis

ssessment

(indeed, inventory and impacts of LCA, Interpreted by DEA

Fig. 2 – System boundaries and relevant inputs of the chickpea production system.

Table 2 – Life cycle inventory data for chickpea production.

Inputs Unit Average Lower bound Upper bound Selection based on

Seed kg 65.99 55 80 Minimum and maximum consumption in the regionNitrogen (N) kg 125.59 100 150 Minimum and maximum consumption in the regionPhosphate (P2O5) kg 134.27 100 180 Minimum and maximum consumption in the regionPotassium (K2O) kg 81 50 100 Minimum and maximum consumption in the regionFYM kg 1636.36 0 5000 Exporters’ suggestions for the regionHerbicide kg 1.37 0.86 2.17 Minimum and maximum consumption in the regionInsecticide kg 1.91 0.66 3.00 Minimum and maximum consumption in the regionFungicide kg 3.14 1.87 5.00 Minimum and maximum consumption in the regionMachinery kg 4433.91 2400 12,231 Farmers’ experiences and degree of farm mechanizationDiesel fuel L 118.10 73 174 Farmers’ experiences and degree of farm mechanizationLabor hr 201 245 170 Exporters’ suggestions for the regionWater m3 289.27 250 320 Minimum and maximum consumption in the regionElectricity kWh 568.22 500 650 Minimum and maximum consumption in the region

Table 3 – Environmental impacts affiliated with the production of chickpea.

Impact categories Nomenclature Measurement units Equation Reference

Acidification potential ACP kg SO2 eq. ACP ¼ PiAPi �mi

a,b (8) [12]

Eutrophication potential EUP kg PO42_ eq. EUP ¼ P

iEPi �mi

c (9) [12]

Global warming potentiald GWP kg CO2 eq. GWP ¼ GWPa;i �mie (10) [12]

Human toxicity potentiald HTP kg 1,4-DCB eq.e HTP ¼ Pi

Pecom

HTPecom;i �mecom;ig,h (11) [12]

Terrestrial ecotoxicity potentiald TEP kg 1,4-DCB eq.f TEP ¼ Pi

Pecom

TETPecom;i �mecom;ig (12) [12]

a APi = Acidification Potential for substance ‘i’ emitted to the air.

b mi = The emission of substance ‘i’ to air, water or soil.

c EPi = The EUP for substance ‘i’ emitted to air, water or oil.

d Considering 100 years.

e GWPa,i = The GWP for substance ‘i’ integrated over ‘ a ’ years (a = Considering 100 years).

f DCB = dichlorobenzene.

g HTPecom,i and TETPecom,i = The HTPecom,i and TETPecom,i (the characterization factor) for substance ‘i’ emitted to emission compartment ‘ecom’

(=air, fresh water, seawater, agricultural soil or industrial soil).

h mecom,i = The emission of substance ‘i’ to medium ‘ecom’.

194 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 195

pivotal role to carry out a LCA study. Emissions have been cal-

culated and converted into the measurement units of each

impact category (characterization factors). Some impact cate-

gories have been employed previously by other researches

[14,15]. SimaPro V8.0 software was used to analyze the envi-

ronmental profile of chickpea production.

2.4. Selected DEA model

DEA compares each producer with only the ‘‘best” producers.

In the DEA literature, a producer is usually referred to as a

DMU. An inefficient DMU can be made efficient either by

decreasing the amount of inputs while retaining the output

constant (input oriented); or symmetrically, by increasing

the amount of output while holding the level of inputs

constant (output oriented) [16]. In this study, input-oriented

DEA seems more proper, given that it is more possibility to

ratiocinate that in the agricultural segment a farmer hasmore

control over inputs rather than output levels.

There are two kinds of DEA models included: CCR and BCC

models. Charness et al. [42] expand CCR model based on con-

stant returns to scale (CRS) and measure the TE of a DMU. On

the other hand, Banker et al. [43] introduced the BCC model

based on variable returns to scale (VRS) and has been devel-

oped to measure PTE.

TE is calculated as follows:

Max hk ¼Ps

r¼1ðurkyrkÞPmi¼1ðvikxikÞ

ð13Þ

Subject to:

Psr¼1ðurkyrkÞPmi¼1ðvikxikÞ

6 1; j ¼ 1; . . . :;n

urk; yrk P 0; r ¼ 1; . . . ; s; i ¼ 1; . . . ;m

where ‘k’ is the DMU being evaluated in the set of j = 1, 2, . . .,

n; ‘x’ is the amount of input; ‘y’ is the output produced; ‘m’

and ‘s’ represent the number of inputs and outputs respec-

tively produced by the DMUs; and ‘urk’ and ‘vik’ are the matrix

of weights assigned to outputs and inputs, respectively.

Also, the PTE can be estimated by a dual linear program-

ming problem as follows [20]:

Max z ¼ uyj � uj ð14Þ

Subject to:

vXi ¼ 1;�vXþ uY � u0 e 6 0

v P 0; u P 0 and u0 is unconstrained in sign:

where ‘z’ and u0 ’ are scalar and free in sign. ‘u’ and ‘v’ are out-

put and input weight matrixes, and ‘Y’ and ‘X’ are corre-

sponding output and input matrixes, respectively. The

letters ‘xi’ and ‘yi’ refer to the inputs and output of jth DMU.

Therefore, SEF gives quantitative information of scale

characteristics; it is the potential productivity gain from

achieving optimal size of a DMU. SEF can be calculated by

the relation between TE and PTE as below [44]:

SEF ¼ TEPTE

ð15Þ

In the current case study, the DEA is used to identify the

inefficient units of energy used. Also, combination of LCA

and DEA methodology helps skippers and operators of these

cultivation systems to be aware of their wasteful practices

and of the need to reduce consumption levels in order to

reduce environmental impacts.

Finally, in order to assess the efficiency indices of energy

and environmental, basic information entered to Excel 2007

spreadsheets and then DEA software Efficiency Measurement

Systems (EMS) V.1.3, was applied.

2.5. Selected MOGA model

GA is a search heuristic approach that used to generate useful

solutions to optimization and search problems. GAs simu-

lated the survival of the fittest among individuals over con-

secutive generation. Each generation consists of a

population of character strings that are analogous to the

chromosome. Each individual represents a point in a search

space and a possible solution. The individuals in the popula-

tion are then made to go through a process of evolution. At

each step of this evolution, two members of the population

were randomly chosen as parents, and children are consid-

ered as the next generation. After an initial population is ran-

domly generated, the algorithm evolves the through five

operators which inspired of nature [26]:

1. A population of chromosomes is produced.

2. The fitness is evaluated.

3. A loop is formed to generate new population.

The following are the steps must be repeated until popula-

tion is completed:

(1) Selection,

(2) Crossover,

(3) Mutation,

and

(4) Accepting.

4. The new generating is used to run the algorithm.

5. Stopping criteria are evaluated.

To optimize the multi-objective function, we used from a

branch of EAs, the MOGA. This method is useful in combina-

tion with LCA in order to minimize the environmental

impacts of a product system.

The MOGA method starts with a clear definition of the

objective functions. The five impacts categories including

GWP, EUP, HTP, ACP and TEP, which need to be minimized;

and chickpea yield, which should to be maximum, was

selected as the objective functions. The objective functions

can be generally defined as follows:

Fmax=min ¼Xj

i¼1

CiXi þ a ð16Þ

where ‘Fmax=min’ is the maximizing or minimizing objective

function, ‘Xi’ is the input variables, ‘Ci’ states the model coef-

ficients (regression coefficients) and ‘a’ is the constant coeffi-

cient of the model (Table 4).

Table 4 – The parameters and coefficients of objective functions.

Input Parameters Yield (b) ACP (c) EUP (g) GWP (d) HTP ðkÞ TEP (x)

Constant a 1652.37 73.892 0.467 545.63 74.892 4.230Seed X1 0.923 0.099 0.026 11.151 2.272 0.103Nitrogen (N) X2 0.835 0.035 0.007 7.30 2.589 0.036Phosphate (P2O5) X3 0.136 0.067 0.014 12.17 5.398 0.120Potassium (K2O) X4 0.382 �0.024 �0.004 �4.57 �2.500 �0.017FYM X5 �0.003 0.011 0.003 1.318 0.384 0.012Herbicide X6 31.88 �0.706 �0.148 �100.176 �45.900 �0.661Insecticide X7 3.54 �1.309 �0.307 �156.007 �49.408 �1.354Fungicide X8 8.65 �0.242 �0.061 �15.413 �1.850 �0.194Machinery X9 �0.001 0.015 0.002 2.742 2.504 0.010Diesel X10 1.37 0.021 0.005 1.881 0.187 0.024Labor X11 0.272 �0.026 �0.007 �2.283 0.908 �0.036Water X12 �0.153 �0.002 �0.0006 �0.190 �0.057 �0.002Electricity X13 0.276 0.0005 0.0001 �0.035 �0.127 �0.001

196 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

To determine coefficients of the model, all data about

energy inputs, crop yield and five LCA indices were entered

into Excel 2007 spreadsheets and SPSS V. 20 software pro-

gram. Another factor that must be considered is the variance

inflation factor (VIF). If VIF for one of the variables is around

or greater than 5, it needs to be excluded from the model [45].

The objective function of crop (F1) developed for this study

was to be maximum while five objective functions (ACP (F2),

EUP (F3), GWP (F4), HTP (F5) and TEP (F6)) were to be mini-

mized. MATLAB V7.14 (R2012a) software was used to develop

MOGA. This software finds the minimum of each objective

function when it solves an optimization problem. Therefore,

the first objective function should be multiplied by (�1) as

follows:

F1 ¼ ð�1Þ � af ð1Þ þ b1X1 þ b2X2 þ b3X3 þ b4X4 þ b5X5

þ b6X6 þ b7X7 þ b8X8 þ b9X9 þ b10X10 þ b11X11

þ b12X12 þ b13X13 ð17Þ

F2 ¼ af ð2Þ þ c1X1 þ c2X2 þ c3X3 þ c4X4 þ c5X5 þ c6X6 þ c7X7

þ c8X8 þ c9X9 þ c10X10 þ c11X11 þ c12X12 þ c13X13 ð18Þ

F3 ¼ af ð3Þ þ g1X1 þ g2X2 þ g3X3 þ g3X3 þ g4X4 þ g5X5 þ g6X6

þ g7X7 þ g8X8 þ g9X9 þ g10X10 þ g11X11 þ g12X12

þ g13X13 ð19Þ

F4 ¼ af ð4Þ þ d1X1 þ d2X2 þ d3X3 þ d4X4 þ d5X5 þ d6X6 þ d7X7

þ d8X8 þ d9X9 þ d10X10 þ d11X11 þ d12X12 þ d13X13 ð20Þ

F5 ¼ af ð5Þ þ k1X1 þ k2X2 þ k3X3 þ k4X4 þ k5X5 þ k6X6 þ k7X7

þ k8X8 þ k9X9 þ k10X10 þ k11X11 þ k12X12 þ k13X13 ð21Þ

F6 ¼ af ð6Þ þ x1X1 þ x2X2 þ x3X3 þ x4X4 þ x5X5 þ x6X6

þ x7X7 þ x8X8 þ x9X9 þ x10X10 þ x11X11 þ x12X12

þ x13X13 ð22Þ

Three forms of constraints that can be used in MOGA are

given as below:

� Linear equality: i.e. A� X ¼ B.

� Linear inequality: i.e. A� X 6 B.

� A set of upper (UB) and lower bounds (LB): i.e. lb 6 X 6 ub

Table 2 shows upper and lower bounds which considered

to run the model.

MOGA is capable of searching logical solutions to a multi-

objective problem; As a result, it can find a diverse set of solu-

tions for difficult problems without being dominated by any

other solution. This process is repeated until a termination

condition has been reached. Common terminating conditions

are as follows [23]:

1. A solution is found that satisfies minimum criteria.

2. A fixed number of generations is reached.

3. An allocated budget (computation time) was gotten.

4. The highest ranking solutions fitness is reaching or has

reached a plateau such that successive iterations no longer

produce better results.

5. Combinations of the above.

In this study, we applied terminating of number one.

3. Results and discussion

3.1. Energy use pattern in chickpea production

Table 5 displays how much energy from different sources was

consumed for chickpea production in Esfahan province of

Iran in the growing season of 2014–2015. The average of total

energy input was estimated at 33211.18 MJ ha�1 while on

average 33462.52 MJ ha�1 output energy was obtained. The

results demonstrated that the most significant contributors

to the total energy input were chemical fertilizers

(13254.7 MJ ha�1), electricity (6818.72 MJ ha�1) and diesel fuel

(5645.18 MJ ha�1), respectively, where chemical fertilizers

made up 29.35% of the total input energy followed by electric-

ity (20.4%). N, P2O5 and K2O respectively accounted for 74%,

18% and 8% of the total chemical fertilizers energy consump-

tion. This is in agreement with the results of Salami and

Ahmadi [46], who reported that, the budget of energy for

chickpea production strongly depends on the rate of diesel

fuel and nitrogen fertilizer. In contrast, Patil et al. [47] in India

claimed that the greater shares of input energy were observed

for human labor and bullock pair (28.53%), as majority of

Table 5 – Energy inputs and output for chickpea production in Esfahan, Iran.

Inputs/output Average energy equivalent (MJ/ha) Percentage of each value SD

A. Inputs1. Seed 970.06 2.92 77.672. Chemical fertilizers

a) Nitrogen 9808.65 29.53 889.42b) Phosphorus (P2O5) 2336.34 7.03 314.86c) Potassium (K2O) 1109.70 3.34 228.37

3. Farmyard manure 490.90 1.47 707.044. Chemical

a) Herbicide 328.30 0.98 76.91b) Insecticide 193.50 0.58 51.42c) Fungicide 678.68 2.04 158.85

5. Machinery 799.24 2.40 382.616. Diesel fuel 5645.18 16.99 1377.377. Human labor 473.64 1.42 26.148. Water for irrigation 3557.92 10.71 413.299. Electricity 6818.72 20.53 363.97Total energy input 33211.18 100 4543.46

B. OutputTotal energy output 33462.52 100 1138.26

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operations were done with this force. Following human labor

and bullock pair, seed (25.78%), chemical fertilizer (22.28%)

and pesticides (14.85%) were the main energy consuming

inputs in their study. The cause of difference between men-

tioned study and our study was the using of women labor

(11%) and bullock pair (10%) instead of machinery. Khosh-

nevisan et al. [48] reported that the average of energy con-

sumption and total output energy in watermelon production

was calculated as 53626.19 MJ ha�1 and 56895.50 MJ ha�1,

respectively. The energy used in irrigation system (61%), Plas-

tic (14%) and N-based fertilizers (9%) were responsible for

more than 80% of the total input energy input in this cultiva-

tion. Therefore, irrigation systems used in watermelon pro-

duction should be upgraded and be replaced with modern

ones. Drip irrigation systems can be used instead of flood irri-

gation systems.

Moreover, several studies have recently been performed on

energy consumption of crop production in Iran, showing that

fossil fuels and chemical fertilizers have the largest share in

energy consumption [49–52].

Table 6 shows the energy indices of chickpea production in

Esfahan province of Iran. Average chickpea yield was about

2276 kg ha�1. The ER, EP, SE and NEG of chickpea production

Table 6 – Energy indices of chickpea production.

Items Unit

Yield kg ha�1

Energy ratio –Specific energy MJ kg�1

Energy productivity kg MJ�1

Net energy gain MJ ha�1

Direct energy MJ ha�1

Indirect energy MJ ha�1

Renewable energy MJ ha�1

Non-renewable energy MJ ha�1

in Esfahan province were 1.02, 0.06 kg MJ�1, 14.54 MJ kg�1

and 251.36 MJ ha�1, respectively. The amount of ER indicated

that output energy of chickpea was 1.02 times greater than

total input energy. In another study led by Patil et al. [47] in

India different results were obtained. They reported that ER

and EP of chickpea production were calculated as 10.35 and

0.304 kg MJ�1. Such a big difference was caused by lower

energy consumption in Indian chickpea production. More

specifically, lower use of chemical fertilizers was the main

reason of such a big difference.

Other researchers reported the results of 0.02 for basil [7],

1.1 for watermelon [23] and 2.86 for barley production [29]. In

addition, as can be seen in Table 6, the shares of DE, IDE, RE

and NRE forms from total energy input calculated as 49.47%,

50.53%, 17.34% and 82.66%, respectively.

3.2. LCA results

The comprehensive results of five impact categories are

shown in Figs. 3 and 4. The results well showed that the appli-

cation of agricultural machinery, nitrogen fertilizers, diesel

fuel and FYM played the key role in environmental conse-

quences of chickpea production in the surveyed region.

Average SD

2276.36 77.431.02 0.114.54 1.510.06 0.007251.36 3461.9916495.77 (49.66%) 2064.7316715.41 (50.33%) 2537.515492.83 (16.53%) 1131.5727718.34 (83.46%) 3483.64

Fig. 3 – Life cycle impacts per two FUs.

Fig. 4 – Contribution of inputs to environmental impact categories.

198 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

Based upon the obtained results, GWP was estimated at

3032.60 kg CO2 eq. t�1. Due to the lack of similar studies the

results are compared with other agricultural crops to show

the magnitude of indices estimated. In a study led by Iriarte

et al. [14] in Chile, GWP for sunflower and rapeseed produc-

tions was estimated at 890 and 820 kg CO2 eq. per t of crop.

This high difference can be interpreted by large application

of such agricultural inputs as machinery and chemical fertil-

izers (mainly N) in the production of chickpea. Considering

EUP, emissions from FYM and agricultural machinery showed

a pivotal role in causing EUP. Khoshnevisan et al. [53] con-

cluded that in open field strawberry production, N-based fer-

tilizers (0.17 kg PO43� eq.) and energy used in traction (0.14 kg

PO43� eq.) had the greatest effect on EUP. Also in the impact

category ACP, direct emissions from diesel fuel and chemical

fertilizers ranked first among all input categories (Fig. 4).

Nemecek et al. [54] compared the environmental burdens

of organic farming vs integrated production systems in Swiss.

They showed that the N2O and CO2 emissions from chemical

fertilizers made high contributions to GWP.

Iriarte et al. [14] in Chile concluded that, N-based fertilizers

had the significant effects on the five impact categories of

ACP, EUP, GWP, HTP and TEP in sunflower and rapeseed pro-

ductions. Similarly, Khoshnevisan et al. [35] claimed that

the electricity was the largest contributor to the total emis-

sions caused by greenhouse cucumber production in Iran.

Fig. 5 shows the emission of pollutants to the air, water and

soil. These emissions were calculated as weighting unit in

term of mPt. Here, Pt is an abbreviation of Point which is

the unit of the weighting results with 1000 Pt the total envi-

ronmental impact of one (average) European citizen during

one year [55]. With respect to the obtained results (Fig. 5),

Fig. 5 – Share of emissions for 1 ton of chickpea production,

in Iran.

I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 199

the shares of total emissions for 1 ton of chickpea production

consist of 210.39 mPt (46%) emissions into air, 235.74 mPt

(53%) emission into top soil and 76.54 (1%) emissions into

ground water.

3.3. DEA results

The outcomes of BCC and CCR models showed that 99 (90%)

and 39 (35.45%) chickpea farmers out of 110 farmers who par-

ticipated in our study were recognized as the technically and

pure technically efficient (score of 1), respectively (Fig. 6). It

indicated substantial inefficiency in use of agricultural inputs

in chickpea production.

It is worth mentioning that, there is not variation between

soil texture or soil yield potential efficiency and efficiency of

farmers; because information have been obtained from the

neighboring town ship, with the same geographical location,

same rainfall, the same temperature and same vegetation

cover. Also in this study, farm size did not have significant dif-

ferences on efficiency of farmers. Mousavi-Avval et al. [44]

reported that, from the total of 94 farmers considered in soy-

bean production, 40 farmers (42.55%) had the PTE score of 1.

Moreover, from the PTE farmers 26 farmers (27.66%) had the

Fig. 6 – Efficiency score distribu

technical efficiency score of 1. It was due to their disadvanta-

geous conditions of scale size.

On the other hand, from relatively inefficient farmers, 11

and 40 farmers had PTE and TE in the 0.9–1 range, respec-

tively. Moreover, 31 DMUs obtained the efficiency score of less

than 0.9, which are known as inefficient producers.

Based on the obtained results from the models (Eqs. (11),

(12) and (13)), the average values of PTE, TE and SEF were cal-

culated as 0.998, 0.944 and 0.945, respectively (Fig. 7). The

results showed that, about 6% of total energy inputs could

be saved without reducing the chickpea yield. Nassiri and

Singh [56] applied DEA to determine efficient and inefficient

of farmers in paddy production in Punjab; they reported that

in zone 2, TE, PTE and SEF were at 0.88, 0.91 and 0.96, respec-

tively. Their results showed that, about 12% of total energy

inputs could be saved without reducing the paddy yield.

The inputs–output energy balance of efficient and ineffi-

cient farmers (based on the CCR model) is shown in Table 7.

The results revealed that, the efficient farmers applied less

inputs than inefficient farmers, except for FYM. Also the main

differences between efficient and inefficient farmers were

found to be for application of potassium, insecticide and

machinery. Nevertheless, the production yield for efficient

farmers was found to be 1.35% lesser than that of inefficient

ones. Among the investigated environmental impacts, for

the EUP, GWP and HTP, there is no significant difference

between efficient and inefficient farmers; while, the emis-

sions of ACP and HTP for efficient farmers were higher than

that of inefficient farmers.

3.4. MOGA results

MOGA is capable of finding optimal solutions. Fig. 8(D) has

plotted the Pareto front for the first two objective functions

(EUP and yield) showing that MOGA has been converged.

MOGA numerated 99 optimal solutions by which crop yield

was maximized and the five impact categories were mini-

mized. It should be noted that the following filters were con-

sidered to select the final results;

tion of chickpea producers.

Fig. 7 – Efficiency indices of chickpea production.

Table 7 – Comparison of efficient and inefficient farmers in chickpea production.

Item (Unit) Efficient farmers(Unit ha�1) (A)

Inefficient farmers(Unit ha�1) (B)

Difference (%)(B - A) � 100/B

A. Inputs1. Seed (kg) 942.30 985.31 4.362. Chemical fertilizers (kg)

a) Nitrogen (N) 9502.16 9977.00 4.75b) Phosphorus (P2O5) 2244.15 2386.98 5.98c) Potassium (K2O) 1009.93 1164.50 13.27

3. Farmyard manure (kg) 500.00 485.91 �2.894. Chemicals (kg)

a) Herbicide 313.08 336.66 7.00b) Insecticide 177.31 202.40 12.39c) Fungicide 647.30 695.91 6.98

5. Machinery (kg) 739.27 834.13 11.376. Diesel fuel (L) 5357.27 5803.32 7.687. Human labor (h) 463.36 479.75 3.418. Water for irrigation (m3) 3415.12 3636.36 6.089. Electricity (kWh) 6461.53 6900.00 6.35

B. Outputs1. Chickpea (Kg) 2256.41 2287.32 1.352. ACP (kg SO2 eq.) 90.45 89.25 �1.343. EUP (kg PO4

2� eq.) 2.95 3.11 5.144. GWP (kg CO2 eq.) 2997.14 3120.88 3.965. HTP (kg 1,4-DCB eq.) 2012.17 2003.91 �0.416. TEP (kg 1,4-DCB eq.) 13.32 13.64 2.34

200 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

1. The total inputs calculated from optimum solutions

should be less than the average of the region.

2. The crop yield calculated from optimum solutions should

be lower than the maximum value in the region.

3. The total environmental indices should be less than that

of in the current condition. Therefore, from 99 solutions

obtained by MOGA, 29 optimum solutions were selected

as tabulated in Table 8. Among these solutions the one

optimum solution was determined by selecting the largest

values in each input. Thus the final optimum solution of

the inputs can be found in Table 8 as shown in bold type.

The results revealed that each of five environmental

indices studied, can be reduced significantly while the

crop yield has been increased. Accordingly, in all optimum

conditions, the emissions of ACP, GWP, EUP and TEP are

not only less than the average but also lower than the min-

imum emissions observed in the studied area. For exam-

ple, GWP and HTP, for one ton of chickpea, decreased

from 3032.60 kg CO2 and 2030.63 kg 1.4-DCB to 2750.40 kg

CO2 and 11920.60 kg 1.4-DCB, respectively. Also, based on

the calculated solution in Table 8, the total inputs used

in region can be reduced, that among them, machinery,

human labor, fungicide and FYM have the greatest reduc-

tion with 59%, 50%, 32% and 29%, respectively.

Fig. 8 – Termination criteria of MOGA for finding optimum solutions.

I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 201

Table 8 – The selected results of solutions produced by MOGA (the final optimum values of inputs are shown in bold type).

Seed N P2O5 K2O FYM Herbicide Insecticide Fungicide Machinery Diesel Labor Water Electricity Yield ACP EUP GWP HTP TEP

55.67 111.47 104.57 78.29 1176.20 1.02 1.69 2.37 2924.40 89.1 232.6 286.1 503.78 2442.54 60.46 1.44 2054.27 1398.84 4.9755.86 111.68 105.12 78.76 1183.46 1.10 1.62 2.12 2924.20 89.0 232.1 281.7 504.17 2283.90 59.99 1.35 2405.09 1441.51 4.7355.85 120.58 105.80 79.89 1190.56 1.11 1.55 3.06 2982.10 105.4 237.9 280.6 559.53 2434.07 60.39 1.55 2470.51 1569.83 5.4855.86 115.77 106.04 77.58 1194.51 1.11 1.76 3.07 2984.60 114.2 238.7 28.63 558.54 2435.44 59.25 1.47 2314.04 1445.26 4.0856.58 112.86 104.83 74.40 1171.29 1.12 1.79 2.41 2925.50 94.3 234.6 270.8 535.35 2435.97 60.13 1.61 2444.99 1582.27 5.4256.83 118.39 107.29 79.86 1178.61 1.17 1.79 3.08 3198.70 116.8 239.3 270.9 554.07 2432.67 59.46 1.50 2343.25 1492.34 4.4856.58 114.24 107.36 79.89 1200.75 1.19 1.79 3.05 3013.90 116.4 234.6 268.6 532.02 2417.09 61.20 1.73 2578.40 1756.03 6.8856.09 113.11 113.56 77.57 1188.52 1.12 1.77 2.60 2958.50 103.3 236.7 279.1 554.32 2395.65 60.98 1.85 2546.09 1702.33 6.5856.60 111.72 115.24 74.40 1187.08 1.15 1.77 2.40 2961.60 95.3 237.0 2.88 536.68 2440.51 60.24 1.52 2432.13 1423.03 4.8755.86 122.56 105.25 79.86 1186.26 1.20 1.79 2.2 3296.80 89.0 232.1 272.4 504.30 2442.38 62.50 1.57 2750.40 1906.45 8.7056.09 114.65 106.95 79.89 1179.80 1.25 1.78 3.06 2978.50 114.4 240.6 273.0 563.58 2437.91 59.69 1.38 2370.85 1529.01 4.7956.03 113.11 110.83 79.47 1174.77 1.23 1.77 2.61 2957.90 113.9 238.7 273.7 564.54 2276.17 59.42 1.54 2336.88 1481.71 4.3955.98 112.86 107.31 79.78 1182.12 1.16 1.77 3.07 3000.00 113.6 237.8 271.6 550.19 2203.14 59.56 1.78 2360.39 1492.42 4.5657.26 117.44 107.77 80.33 1179.05 1.11 1.79 3.13 2925.10 108.1 237.7 273.3 550.02 2193.90 61.20 1.66 2329.83 1464.69 4.2555.69 118.93 104.68 77.37 1182.13 1.11 1.75 2.38 3025.60 91.2 234.7 277.7 508.96 2197.35 60.98 1.75 2430.78 1661.18 5.7256.79 112.34 106.56 79.69 1178.69 1.11 1.77 3.08 2948.40 115.9 236.0 277.0 559.01 2405.04 60.24 1.49 2488.69 1631.38 5.8956.23 117.86 107.14 79.29 1198.87 1.10 1.78 2.73 2966.40 93.5 235.5 270.3 545.38 2214.50 62.50 1.51 2392.03 1541.18 5.0056.58 114.23 105.76 72.42 1192.32 1.01 1.79 3.08 3025.60 108.5 234.6 272.1 535.30 2437.68 59.69 1.41 2530.31 1718.60 6.5656.20 113.26 114.21 78.00 1186.97 1.05 1.77 2.40 2957.80 115.7 235.3 273.6 527.60 2208.74 59.42 1.63 2643.04 1787.96 7.6556.62 112.70 107.15 79.56 1197.74 1.06 1.79 3.07 2966.43 101.4 237.9 278.7 557.02 2414.91 59.56 1.76 2516.45 1660.46 6.2755.94 111.81 104.98 74.72 1197.75 1.11 1.79 2.19 2957.20 89.1 232.3 282.9 518.27 2437.39 59.38 1.62 2721.24 1846.61 8.1358.57 116.29 105.09 79.91 1198.54 1.11 1.77 2.18 3110.80 112.0 239.0 279.5 560.95 2283.71 60.19 1.59 2347.98 1513.32 4.6355.86 113.39 106.06 80.18 1187.06 1.05 1.78 3.07 2924.50 109.1 237.8 285.7 556.66 2336.17 60.56 1.40 2598.54 1744.01 7.2056.30 123.56 107.30 80.37 1201.85 1.03 1.79 3.09 3108.60 116.9 236.9 271.0 565.33 2444.88 59.75 1.44 2740.02 1920.60 8.7156.14 118.99 105.76 80.18 1196.05 1.03 1.77 2.90 2989.50 96.83 235.6 280.7 563.20 2279.32 60.85 1.72 2455.11 1560.41 5.4356.58 113.11 105.46 72.94 1190.14 1.03 1.79 2.62 2921.10 102.7 234.6 269.4 535.34 2425.90 61.46 1.57 2695.35 1825.20 8.0855.99 118.43 109.21 78.77 1198.52 1.01 1.77 3.04 2986.80 105.2 238.9 284.6 564.88 2398.39 60.70 1.49 2557.47 1637.76 6.2056.42 112.59 104.82 79.66 1196.09 1.05 1.77 2.39 2925.40 104.9 234.6 270.0 522.98 2298.74 62.14 1.41 2682.69 1807.76 7.8356.25 119.07 108.10 79.80 1198.41 1.06 1.76 3.09 3099.40 114.9 239.3 280.1 562.97 2436.79 59.50 1.47 2415.54 1411.90 4.74

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I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5 203

Finally, despite higher consumption of inputs, crop yield is

estimated at 2444.88 kg ha�1, which is equal to the region’s

maximum under current condition. Applying new agricul-

tural machineries and tractors with higher field capacity

and using better approach in seedbed preparation (like

reduced tillage systems) and in sowing operations instead of

conventional methods, are highly recommended as practical

solutions for decreasing energy consumption and negative

environmental burdens caused by excessive use of different

sources of energy in chickpea production in the studied area.

Also, the models offered by Shamshirband et al. [23] indi-

cated that inputs of P2O5, N, K2O and machinery can poten-

tially be reduced by 58%, 54%, 45% and 40% in watermelon

production. As presented in Table 8, all of the applied agricul-

tural inputs were fully efficient where in reality it is not pos-

sible that a system treats totally efficiently but it can be a

good help for farmers to find feasible ways for the reduction

of agricultural inputs. When a system is optimized, it is likely

that do not optimized all inputs but we can to combine differ-

ent application of inputs in a way that all objective functions

are met simultaneously [23].

3.5. Comparison of DEA and GA for energy inputs

In order to optimize the best combination of inputs, the low-

est energy input is dedicated to the optimal combination was

selected. As can be seen in Table 9, the total input energy for

chickpea will be equal to 27,570 MJ ha�1 (17% of energy

saving).

On the other hand, the outcomes of DEA showed that the

total energy input can be decreased to the value of

31,511 MJ ha�1 (5.10% energy saving). It means that, GA was

superior to DEA for finding optimal patterns of energy usage

and reducing environmental impacts. The interesting point

that is clear in this table, is the inability of DEA in optimiza-

tion of FYM; So that, the amount of the input energy in the

optimal value is higher than the actual value (-1.85% of

energy saving). But unlike the DEA, GA was able to save

Table 9 – The actual and optimal amounts of energy inputs in c

Inputs Average actualenergy (MJ ha�1)

1. Seed 970.062. Chemical fertilizers

a) Nitrogen 9808.65b) Phosphorus (P2O5) 2336.34c) Potassium (K2O) 1109.70

3. FYM 490.904. Chemical

a) Herbicide 328.30b) Insecticide 193.50c) Fungicide 678.68

5. Machinery 799.246. Diesel fuel 5645.187. Human labor 473.648. Water for irrigation 3557.929. Electricity 6818.72

Total energy input 33211.18

FYM energy by 29%. Shamshirband et al. [23] employed the

MOGA technique to minimize GHG emissions and maximize

output energy of watermelon production. The results

revealed that on average, 28% of the total energy input and

33% of the total GHG emissions in watermelon production

can be reduced. On the other hand, optimization results with

DEA, will decrease the consumption of energy inputs to

31,511 MJ ha�1 (5.10% of energy saving). In another study by

Pahlavan et al. [57] in Iran, by optimization of energy con-

sumption in rose production with DEA approach, on an aver-

age, about 43.59% of the total input energy could be saved

without reducing the rose yield.

4. Conclusions

In this study, the ability of DEA and MOGA techniques was

investigated in optimization of energy consumption and envi-

ronmental impacts of chickpea production in Esfahan pro-

vince of Iran. Summary of conclusions can be stated as

follow:

– Total energy inputs and output for chickpea production

were 33211.18 and 33462.52 MJ ha�1, respectively. The

energy input of chemical fertilizer (39.90%), mainly nitro-

gen, had the greatest share within the total energy inputs

followed by electricity (20.53%). Accordingly, ER, EP, SE

and NEG in chickpea production were 1.02, 0.06 kg MJ�1,

14.54 MJ kg�1 and 251.36 MJ ha�1, respectively.

– The LCA study showed that the most significant impact

categories are ACP, EUP, GWP, HTP and TEP which they

are related to the use of agricultural machinery for seedbed

preparation and sowing operations, followed by the diesel

fuel consumed, chemical fertilizers and FYM in the studied

region. Therefore it expected that, using No-till and

reduced tillage system, clean fuels such as biodiesel and

bio-ethanol instead of fossil fuels and more efficient fertil-

izers application by Integrated Nutrient Management, not

hickpea production system.

Average optimal energy (MJ ha�1)

(DEA) (GA)

942.30 818.34

9502.16 8705.702244.15 1819.511009.93 992.15500 351.38

313.08 240.38177.31 156.86647.30 457.92739.27 326.015357.27 4257.54463.36 232.143415.12 2680.636461.53 6045.36

31511.72 27570.61

204 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 3 ( 2 0 1 6 ) 1 9 0 –2 0 5

only for reducing negative effects to environment, human

health, maintaining sustainability, but also for providing

higher energy use efficiency.

– Based on the results of DEA, the average values of PTE, TE

and SEF were calculated as 0.998, 0.944 and 0.945, respec-

tively. Also, potassium fertilizer, insecticide andmachinery

energy inputs had the highest potential for saving energy;

so, if inefficient farmers followed efficient farmers, they

would significantly improve their ER.

– In order to optimize the energy crop yield and studied

environmental impacts by MOGA, of all agricultural inputs,

machinery held the first rank with a reduction of 59%. It

shows that machinery management technique in this cul-

tivation is not efficient at all. On the other hand, the values

of environmental impacts of ACP, EUP, GWP, HTP and TEP

using MOGA, reduced up to 29%, 23%, 10%, 6% and 36%,

respectively.

– In the optimum condition, total energy inputs for chickpea

production achieved to 5.11% of saving energy using DEA.

However, about 17% of total energy inputs could be saved

without reducing the chickpea yield through GA.

– From the results obtained, it is concluded that there is a

great potential for reducing energy consumption and envi-

ronmental impacts in the chickpea production and opti-

mization by MOGA was significantly better than the

optimization by DEA approach.

Acknowledgment

The financial support provided by the University of Tehran,

Iran, is duly acknowledged.

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